Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Abstract Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont’s stress response, and have the potential to advance the field further.


Introduction
Whereas theories about how eukaryotic biological complexity ar ose ar e div erse, one common thr ead among them is the need for cooper ation, inter action, and m utual dependence between multiple species (Bosch and McFall-Ngai 2011 ).In the case of corals, this involves billions of cells from every domain of life, commonly r eferr ed to as the holobiont.By virtue of the algal endosymbiosis and subsequent metabolic sharing between holobiont members , reefs ha ve come to cover 255 000 km 2 of the Earth's surface (Spalding and Grenfell 1997 ) and accommodate an estimated 7000 marine species in oligotrophic waters (Fisher et al. 2015 ).Modern cor al r eefs pr otect coastlines fr om storms and er osion (Beck et al. 2018 ) while providing economic prosperity to local comm unities thr ough food suppl y and tourism.The economic value of coral reefs is difficult to appraise, but even modest estimates ($350 000/ha per year) are 70 times higher than rain forests (Costanza et al. 2014 ).
Cor als hav e coe volv ed (Ritc hie and Smith 2004 , Rohwer and K ell y 2004 ) with diverse yet specific populations of micr oor ganisms (Rohwer et al. 2002, Koren and Rosenberg 2006, Bourne et al. 2016 ).These species can either confer beneficial genes and traits (Lesser et al. 2007, Neave et al. 2017, Zhang et al. 2021 ) or cause disease and holobiont dysfunction (Garren et al. 2014, Ainsworth et al. 2017, Klinges et al. 2020 ).The cor al micr obiome is a combination of the micr oor ganisms, v arying in species and density, their combined genetic material, and their active metabolism (Wang et al. 2018 ).The core composition of this community is lar gel y deter-mined by the host (K ell y et al. 2014 ) to cultivate metabolic adaptations to local environmental conditions through the selection of beneficial genes (Ainsworth et al. 2017 ).The functions of the holobionts' microbial component are known to provide protection against pathogens (Rohwer et al. 2002 , Shnit-Orland andKushmaro 2009 ), cycle carbon, nitrogen, sulfur, and phosphate (Bourne et al. 2016 ), and provide bioavailable forms of trace metals, vitamins, and other cofactors (Phillips 1984 ).To this end, the host possesses se v er al distinct habitats suited for micr obial r esidence (Fig. 1 ): the tissue (epidermis and gastrodermis; (Lesser et al. 2007 ), the gastric cavity (Herndl et al. 1985 ), the skeleton (Shashar et al. 1997 ), and the surface mucus layer (SML; Rohwer et al. 2002, Kooperman et al. 2007 ).
The loss of coral reefs is equivalent to the loss of r ainfor ests in terrestrial systems.Although the holobiont has adapted and survived past climate fluctuations , current en vironmental changes caused by anthropogenic climate c hange, suc h as incr eased sea surface temper atur e and ocean acidification, hav e pr ov en to be beyond anything since the last glaciation (Pandolfi et al. 2011 ).For the aforementioned reasons, there is a heightened focus on research dedicated to improving the resilience of coral reefs in the face of current and future stresses (Bourne et al. 2016 ), accelerating the need for a detailed understanding of holobiont group dynamics and responses to stress.
An integral part of the coral holobiont, especially during times of str ess, ar e the algal endosymbionts .T he role of the algal endosymbiont population in relation to bleaching susceptibility is Figure 1.Illustration of a stony coral holobiont, displaying a cross-section of the coral polyp to show the detailed structure of the tissue la yers , surface m ucopol ysacc haride layer (SML), and skeleton, in addition to the microbiome components associated with each feature of the polyp.Corals have radial body plans consisting of epidermal and gastrodermal epithelia, mesoglea, and a gastr ov ascular cavity.Stinging tentacles and nematocysts are used by the polyp to catch prey, mainly zooplankton.Coral animals, in conjunction with symbionts, secrete aragonite crystals from the base of the polyp to form the skeletal structure.Symbiodiniaceae inhabit the gastrodermis layer.Algal symbionts are encased in a host-derived membrane called the symbiosome, which allows the transport of metabolites between the alga and cor al. Cor als will acidify the symbiosome micr oenvir onment (pH ∼4) in order to promote photosynthesis .T he densest microbial community exists in the SML and includes many archaea, bacteria, and algal species, as well as viruses, as r epr esented by an abundant array of microbial species .T he SML is composed of glycoproteins and sulphated oligosaccharides connected via glycosidic linkages.It is enriched in nitrogen and organic material, providing a rich food source for the microbiome.In return, the SML micr obial comm unity pr otects the cor al fr om inv asiv e pathogens , cycles nutrients , and pro vides bioa v ailable forms of tr ace metals , vitamins , and other cofactors .T he micr obial comm unity of the SML is distinct fr om micr obial pr ofiles of the adjacent seawater, indicating that the host exhibits contr ol ov er its micr obiome members.Because the SML r egularl y sloughs off fr om the cor al surface, holobiont micr obial comm unities m ust be dynamic and well-adapted to participating in this ecosystem.Endolithic prokaryotes, fungi, and filamentous algae associated with the skeleton are depicted as green filaments .T he species in the genus Ostreobium are the most abundant members of the skeletal micr obiome.Cor al-associated micr obial a ggr egates (CAMAs) r eside in the epidermis and gastr odermis layers ar e r epr esented as clumps of blue cells .Although C AMAs ha ve been described as facultative symbionts, they are prevalent and abundant in coral tissue, suggesting they do help maintain coral fitness.m ultilayer ed, as bleac hing risk depends on man y aspects, suc h as the symbiont species pr esent, (Gl ynn et al. 2001 ), the effect of endosymbiont density (Cunning andBaker 2013 , Scheufen et al. 2017a ), and the adaptation of the coral holobiont to the stress (Cunning and Baker 2013, Scheufen et al. 2017a,b , Barott et al. 2021, Vidal-Dupiol et al. 2022 ).The endosymbionts ar e dinofla gellates belonging to the family Symbiodiniaceae.Endosymbiont species differ in their growth rates, photosynthetic abilities, host specificity, number of c hr omosomes, and allozyme alleles (Blank and Tr enc h 1985, Stat 2010, LaJeunesse et al. 2018 ).The symbiont community composition is distinct for each coral species at a given location (Ho w ells et al. 2020, de Souza et al. 2022 ) and eac h pr ofile confers v arying tr aits to the cor al animal (Wall et al. 2020, Torres et al. 2021 ).This is partially because symbiont species vary in their rate of proliferation (Stat 2010 ), control over hostalgal nutrient exchange (Stat et al. 2008, Aranda et al. 2016 ), and photosynthetic rate during environmental stress (Davy et al. 2012, Scheufen et al. 2017a, Liu et al. 2018 ).It is important to note that endosymbiont physiology, particularly in terms of their photosynthetic rate, is itself mediated by the optical and biological interactions within the host, so defining the coral-symbiont-skeleton unit is essential when discussing photosynthetic rate (Scheufen et al. 2017a ).Working with four differ ent cor al species, Sc heufen et al. ( 2017a ) found that combinations of skeleton morphology, tissue thic kness, v ariation of cor al pigmentation, species plasticity for changing symbiont content, dominant endosymbiont type, and symbiont cell pigmentation contribute to the rate of algal photosynthesis, and ther efor e the risk of bleaching.This study supported the idea that "pigment pac ka ging," or the distribution of pigmentation over cell area, within coral tissue is a coralsymbiont dynamic that presents a relevant species-specific component to v arious str ess r esponses and bleac hing risk.This c hange in vulnerability for corals provides a quantitative, mechanistic link between algal endosymbiont metabolism and the molecular basis for coral bleaching.Ho w ever, the mechanism of coral bleac hing r emains unknown.
Beyond the algal endosymbionts, there is evidence that holobiont micr obial comm unities ar e dynamic and well-ada pted to participating in this ecosystem (Rohwer et al. 2001, 2002, Frias-Lopez et al. 2002, Reshef et al. 2006, Rosenberg et al. 2007, Bythell and Wild 2011 ).Ho w e v er, the adv anta ges the micr obiome affords the cor al a ppear to fade when the holobiont is exposed to stress (Sunagawa et al. 2010, Littman et al. 2011 ).This destabilization is supported by a change in diversity (Thurber et al. 2009, Zane v eld et al. 2017 ) and a concomitant shift to w ar d opportunistic microorganisms and pathogens (Ritchie 2006, Littman et al. 2011 ); yet, the contr olling mec hanisms behind the destabilization has yet to be unveiled.
Although the general interactions between the coral host and micr obiome ar e documented, the contr olling mec hanisms and metabolic pathways operating within the holobiont stand unexplained at all stages of life .T here is value in gaining a multifaceted understanding of the coral holobiont through multiomics a ppr oac hes (i.e .genomics , transcriptomics , metabolomics , proteomics, and metagenomics; Fig. 2 ; Table 1 ) to answer questions about the roles of holobiont residents, how different stressors affect members of the holobiont, especially at different stages of cor al de v elopment, the formation and deterioration of the symbiotic relationships, and ultimately identify biomarkers of coral stress and discover ways to encourage coral resilience .T his revie w cov ers how these tec hniques hav e been a pplied to the cor al holobiont, their limitations, and ways to ov ercome constr aints, adv ance anal yses, and ultimatel y optimize the data acquir ed.

Coral studies and omics techniques
Ther e ar e man y hurdles to effectiv el y ca ptur e an accur ate ima ge of holobiont interactions and physiology.One major obstacle is the inability to separate holobiont members for individual, in situ , omics analyses.While metagenomics of the microbiome through its separation and/or enrichment is a worthwhile pursuit (Robbins et al. 2019 ), altered physiological states introduced during the time and processes required to separate and enrich microbial species call into question the accuracy of other omics techniques used to observe metabolic activity (i.e .transcriptomics , proteomics , metabolomics , and metadata).How this issue pertains to each omics method specifically is discussed in the ensuing r espectiv e sections.

Microbial taxonomic identification techniques
To discern the identities of microbial holobiont residents, marker gene analysis (amplicon sequencing) and shotgun sequencing hav e historicall y been emplo y ed.Marker gene analysis relies upon primers designed to bind to highly conserved genes or regions within these genes, amplification of the gene, and sequencing libraries for taxonomic profiling and quantifying of a species' relative abundance.For example, 16S rRNA (bacteria and archaea) and ITS2 (algal symbionts and fungi) region amplicon sequencing has been used to survey the composition of the coral microbiome and its flux with c hanging envir onmental conditions, seasons, and time (Sweet et al. 2011, Arif et al. 2014, Apprill et al. 2016, Wang et al. 2018, Dunphy et al. 2019, Maher et al. 2019, Klinges et al. 2022 ).Ho w e v er, this a ppr oac h is not without error, as many biases ar e intr oduced during PCR amplification and sequencing.Namely, primers do not exhibit equal affinities for all DNA sequences because of GC bias or ov er all abundance of sequences present (i.e. the more a sequence is present, the more likely it is to be amplified, and ther efor e mor e likel y sequenced; (Br ooks et al. 2015 ).Mor eov er, these studies onl y allow speculation concerning the metabolism of the microbiome and its interactions with the coral host and surrounding environment.
Metagenomics can distinguish the genetic potential of microbiome members through providing genome-resolved phylogenic data (Thurber et al. 2009, Littman et al. 2011, Badhai et al. 2016, Cissell and McCoy 2021, Palladino et al. 2022 ).Again, this technique is not without its faults, particularly when applied to the holobiont.The micr obiome-r elated sequences can be easily overwhelmed by the coral genome, which registers the majority of sequence data (Wegley et al. 2007, Robbins et al. 2019 ).For this r eason, separ ation and enrichment methods may be required for metagenomics of the microbiome to be fruitful (Thurber et al. 2009, Robbins et al. 2019, Palladino et al. 2022 ).
Employing techniques developed in other fields is one way to pr opel cor al micr obial identification forw ar d.One such example is cultur omics, whic h w as established b y human gut microbiologists to identify unknown microbial species (Seng et al. 2009, Lagier et al. 2012, 2016, Diakite et al. 2019 ).Cultur omics r elies on high-throughput culturing techniques, matrix-assisted laser desorption/ionization-time of flight (MALDI -TOF) mass spectrometry (MS), and 16S RNA sequencing to identify new species if initial identification fails (Seng et al. 2009 ).Samples are first divided and div ersified into v arious cultur e conditions for pr olonged incubation times to stimulate the growth of species present at lo w er concentr ations, or an y specific taxa, befor e using MALDI-TOF MS to identify taxa (Lagier et al. 2018 ).The successful use of this time and cost-effective platform relies upon ever growing databases containing the protein mass spectra of each species, preferably Figure 2. Schematic of the omics methods described in this review and information gained in each omics dataset.Genome, metagenome, and transcriptome data is produced via sequencing technologies.Genomics data (DNA) provides information about an organism's potential, as well as genetic variation (copy number variants, single nucleotide polymorphisms, and genomic rearrangements), and in the case of metagenomics, species diversity and abundance .T he transcriptome refers to the cell's whole set of RNA transcribed from the genome at a given time to gain information about gene expression, as well as the regulation of gene expression.Proteomics and metabolomics utilize mass spectrometry for large-scale experimental analysis of the proteome and metabolome, respectively.Proteomics allows for expression profiling of proteins translated from RNA, in addition to acquiring information about their structures and regulation.Metabolomic data includes the most diverse set of compounds out of all the omics a ppr oac hes as it is the final functional r eadout of the or ganism.Ev erything fr om the sample pr epar ation to the instrumentation used will determine, which portion of the metabolome is measured for metabolite profiling.Both proteomic and metabolomic data can be analyzed for potential biomarkers of specific phenotypes.
fr om m ultiple isolates .When identification via MALDI-T OF MS is unsuccessful, 16S rRNA sequencing is then used to identify new species that are then added to the database.In one study, fecal samples were incubated with ethanol to enrich the abundance of spor es, r esulting in the gr owth of 137 sporulated bacterial species, of which 69 were new taxa (Browne et al. 2016 ).More recentl y, cultur omics has been implemented beyond clinical microbiology to isolate 40 bacterial species from the planarian flatworm Schmidtea mediterranea (Kangale et al. 2021 ), 18 species from table salt, including one novel species (Diop et al. 2016 ), and 17 bacterial str ains pr esent in soil ca pable of degr ading diesel oil, bitumen, and m ultiple pol ycyclic ar omatic hydr ocarbons (Chicca et al. 2022 ).

Holobiont transcriptomics
The advent of sequencing transcriptomic responses has radically alter ed the anal ytical and experimental model of coral biology more than any other omics technique (Miller et al. 2011 (Yuan et al. 2017, Zhou et al. 2018, Poquita-Du et al. 2019, Xiang et al. 2019, Bollati et al. 2022 ) and disease (Libro et al. 2013, Vidal-Dupiol et al. 2014, Daniels et al. 2015, Frazier et al. 2017, Kelley et al. 2021, Tra ylor-Knowles et al. 2021). P oquita-Du et al. ( 2019 ) subjected Pocillopora acuta to both heat and sediment treatments.RNAseq data findings included the disruption of genes related to cilia assembly and disassembly, the downregulation of genes involved in cell adhesion, and highl y unr egulated innate and ada ptiv e imm une r esponses in samples exposed to heat and sediment stress compar ed to contr ols.When comparing Orbicella f aveolata and Montastraea cavernosa colonies with and without stony coral tissue loss disease (SCTLD), which has infected almost one third of Caribbean coral species and half of Florida's coral species to date (Skrivanek and Wusinich-Mendez 2020 ), Traylor-Knowles et al. ( 2021 ) discov er ed differ entiall y expr essed genes (DEGs) in the diseased cor als wer e functionall y enric hed for pathways associated with hormone synthesis and signaling, such as Wnt and mTOR.
Studies utilizing transcriptomics have also examined basic coral biology to better understand coral reproduction (Williams et al. 2021b , Zakas and Harry 2022 ), life stages (Meyer et al. 2009 ,  Table 1.Details for the omics methods discussed in this r e vie w.Eac h omics method and the technologies used for each method are explained, and a description of the resulting data is provided.

Genomics and metagenomics
The study of all DNA within an organism (genomics), or within a group of organisms (metagenomics).This includes gene interactions with other genes and the en vironment.T he data can be used to understand an organism's identity, abundance, potential, and evolutionary history.

Marker gene analysis
Uses primers designed to bind to highly conserved genes or regions within these genes, amplification of the gene, and sequencing libraries for taxonomic profiling and quantifying of a species' relative abundance.Shotgun sequencing Involv es r andoml y br eaking up the DNA, whic h is then sequenced and reassembled using bioinformatic tools .T his method can distinguish the genetic potential of microbiome members through providing genome-r esolv ed phylogenic data.

Culturomics
Relies on high-throughput culturing techniques, matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (MS), and 16S RNA sequencing to identify new species if initial identification fails.

Transcriptomics and metatranscriptomics
The study of all RNA within an or ganism (tr anscriptome), or group of organisms (metatranscriptomics), at a given time .T he data pro vides information about gene expression, as well as the regulation of gene expression.

RNA-Seq
Uses next-generation sequencing to sequence RNA that has been isolated and r e v erse tr anscribed into cDN A, or complementary DN A. Library pr epar ation can include steps to enrich RNA with 3 polyadenylated tails (polyA selection) or filter RNA that binds to specific sequences, such as microRN A (miRN A).The resulting sequences are either assembled de novo or mapped to genomes .T he data allow studies to gain information about changes in gene expression, alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, and mutations/SNPs.

Proteomics and meta pr oteomics
The study of proteins within an organism or group of organisms (meta pr oteomics).Pr oteomics allows for expression profiling of pr oteins tr anslated fr om RNA, in addition to acquiring information about their structures and regulation

Mass spectrometry
Pr oteins ar e first isolated fr om samples befor e pr oteins ar e pr epar ed for measur ement thr ough MS.Two MS-based methods ar e curr entl y used.The first uses 2D-electr ophor esis to separate proteins within a sample, follo w ed by selecting and staining proteins of interest, using based on differential expression or general abundance .T he proteins are then identified using MS.The second method utilizes stable isotope tagging to label proteins differ entiall y befor e the pr oteins ar e digested and the labeled peptides analyzed via tandem MS.Proteomics allows for expression profiling of pr oteins tr anslated fr om RNA, the determination of what mRNA is translated into proteins, information about protein interactions, and the identification of any posttranslational modifications.

Metabolomics
The study of all metabolites within an organism or group of organisms.Metabolomic data includes the most diverse set of compounds out of all the omics a ppr oac hes as it is the final functional readout of the organism.

Mass spectrometry
MS methods involve extracting metabolites from samples which are then analyzed by the MS instrument.Portions of the metabolome can be isolated to ca ptur e differ ent kinds of molecules based on size or polarity.MS-based methods will obtain the most extensive metabolome coverage compared to NMR, with the most versatile technique incorporating High Performance Liquid Chr omatogr a phy (HPLC).Liquid c hr omatogr a phy-mass spectr ometry (LC-MS) will detect any polar, ionic, thermally labile, and nonvolatile organic compound that undergoes sufficient ionization.Specific MS signatures can be acquired through the addition of tandem MS (MS/MS), which improves metabolite identification.Gas-liquid partition c hr omatogr a phy (GLPC), commonly known as gas chromatography-mass spectrometry (GC-MS), is another analytical method able to measure an extensive range of analytes that are volatile and thermally labile.Nuclear magnetic resonance spectroscopy (NMR) As with MS, metabolites are first extracted.NMR operates on the principle that nuclei are electrically charged and have spin, so when an external ma gnetic field (r adio wav es) is a pplied, the ener gy is tr ansferr ed to the nuclei, promoting it to a higher energy level.Energy is emitted at the same frequency (as nuclear magnetic resonance) it was absorbed when the spin returns to its ground level and detected with radio receivers.Numerous NMR tec hniques ar e av ailable, including onedimensional (OD) 1H NMR, two-dimensional (2D) NMR, and 13C NMR.NMR can detect all organic compounds with metabolite concentration proportional to signal intensities, albeit at lo w er sensitivities when compared to MS methods, especially for complex samples.Ho w e v er, NMR has the added adv anta ge of elucidating extensiv e structur al information to aid in unknown identification, c hir ality, and position specific isotope enrichment studies.Portune et al. 2010, Siboni et al. 2014, Strader et al. 2018, Chille et al. 2021, Yoshioka et al. 2022 ), and biomineralization (Mass et al. 2016, Drake et al. 2021, Neder et al. 2022, Han et al. 2022 ).Strader et al. ( 2018 ) profiled Acropora millepora gene expression in embryos and larvae for 12 da ys postfertilization.T here was a rise in larval response to settlement cues throughout development as well as the upregulation of signal transduction and sensory genes.In another study, while investigating how similar biomineralization mechanisms produce such varied morphological differ ences acr oss four cor al species-Pocillopora damicor-nis , Pocillopora verrucose , Acropora muricata , and Montipora foliosa- Han et al. ( 2022 ) found that carbonic anhydrase 2 was expressed at high le v els univ ersall y.Ho w e v er , calcium A TPases and bicarbonate tr ansporter expr ession le v els fluctuated, whic h implies that Ca 2 + and HCO 3 transport rates contribute to morphological variances.
As far as transcriptomics has advanced coral research, issues related to conclusions based entir el y on sequenced mRNA should not be disregarded.Assembling short reads without a r efer ence genome sequence into a de novo transcriptome is complicated by paralogs , alleles , and other processes such as alternative splicing (Miller et al. 2011 ).Highl y r e petiti ve genomic regions (Miller et al. 2011 ) and pol ymor phism pr esent in many corals (Shinzato et al. 2015, Drury et al. 2016, Quigley et al. 2020, Zayasu et al. 2021, Poliseno et al. 2022 ) specifically complicates the assembl y pr ocess of corals .T hese impediments can be r esolv ed with a high-quality genome, which exists for few coral species, but still does not establish the actual protein abundance present, as it has been shown that mRNA expression often does not equate to protein expression (Williams et al. 2023 ).
As the genes of the activ e micr obial populace contribute to the genetic potential of the holobiont, the microbial metatranscriptome holds information regar ding ho w the holobiont responds to external factors to identify ecologically relevant functions.Unfortunatel y, obtaining pr okaryotic tr anscripts with adequate cov erage of the coral microbiome is specifically challenging.This is because most transcripts originate from the coral animal, and as pr e viousl y mentioned, micr obial enric hment pr actices alter the metatranscriptome .T he high cost of library preparation and sequencing compound this problem, as does the lack of standard methods for RNA extr action, stor a ge, and pr epar ation, whic h impact transcript and taxa r ecov ery (Cle v es et al. 2020 ).Further, e v en in simpler systems, there has been evidence that some microbial mRNAs are poorly translated because they have weak ribosomal binding sites (Liang et al. 2000 ).Ther efor e, beyond sequencing biases, the microbial metatranscriptome may not include mRNA involved in the holobiont phenotype and its response to perturbations.
Sequencing Symbiodiniaceae-specific transcriptomes is aided by the application of PolyA selection (Moitinho-Silva et al. 2014, Daniels et al. 2015 ), but most transcripts still belong to the coral host, and analysis of what transcripts remain is hindered by the lac k of accur ate r efer ence algal genomes (Lin 2011 ).Symbiodiniaceae genomes are large (1-5 Gbp) with idiosyncratic features due to a combination of neutral selection and symbiotic associations varying by host specificity (Wisecaver and Hackett 2011, LaJeunesse et al. 2018, Forsman et al. 2020 ).Recent analysis of 15 Symbiodiniaceae genome sequences from species ranging from obligate symbiont to free-living revealed that > 95% of core genes in Symbiodinium species and 83.56% of genes in Breviolum , Cladocopium , and Fugacium are "dark," or do not share significant sequence similarity to any UniProt protein sequences (González-Pec h et al. 2021 ).Ther efor e, e v en a significant number of conserved genes within coral symbionts are not annotated, further complicating analysis of metatranscriptomic data.These obstacles have been over come b y numerous studies (Bayer et al. 2012, Ladner et al. 2012, Gust et al. 2014, Li et al. 2020, Mohamed et al. 2020, Yoshioka et al. 2021, Yuyama et al. 2022 ) and can be aided by tools (PSyTrans) and published bioinformatic strategies (Meng et al. 2018 ).Mohamed et al. ( 2020 ) used a dual RNA-sequencing appr oac h to c har acterize cor al-symbiont ( Acropora tenuis and Cladocopium goreaui , r espectiv el y) inter actions during colonization by comparing tr anscript le v els of cultur ed C .goreaui to that of in hos-pite cells .T he anal ysis r e v ealed that in a symbiotic state, imm unerelated and stress response genes were downregulated, but overall metabolism was upregulated.

Holobiont proteomics
Although the presence of most proteins detected thr ough pr oteomics will be ca ptur ed as mRNA, meta pr oteomics data can answer questions about what mRNA is translated into proteins comprising the proteome, the direct quantity of proteins present, protein interactions, and any posttranslational modifications, which may be extreme enough to completely change the functionality of said protein (Vogel andMarcotte 2012 , Liu et al. 2016 ).Proteomics has been applied to corals to study how the animal responds to envir onmental str ess (Weston et al. 2015, Ricaurte et al. 2016, Stuhr et al. 2018, Mayfield et al. 2021, Lin et al. 2022 ), acclimates to unstable environmental conditions (Mayfield et al. 2018, Janech et al. 2021 ), counters disease (Gochfeld et al. 2015, Zhang et al. 2017, Ricci et al. 2019 ), and forms their ar a gonite skeletal structur es (Conci et al. 2020, Drake et al. 2020, Zaquin et al. 2021, 2022 ).
One study utilizing nine coral species from Key West, Florida r eefs, all highl y susceptible to SCTLD but varying in their disease dynamic, found that the downregulation of green fluorescent proteins in infected corals was consistent regardless of the coral species, r epr esenting its potential as a marker of SCTLD pr ogr ession.(Janech et al. 2021 ).Conci et al. ( 2020 ) a pplied pr oteomics to compare the biomineralization process of calcitic octocoral species ( Tubipora musica and Sinularia cf.cruciata ), an ar a gonitic octocoral species ( Heliopora coerulea ), and an aragonitic scleractinian coral ( Montipora digitata ).The analysis presented little commonality when comparing the pol ymor phs, except for the presence of carbonic anhydrase CruCA4 in both calcitic and ar a gonitic species, and a galaxin-related protein and he phaestin-lik e protein present in both octocorals and scleractinians.Phylogenetic analysis indicated that out of the three proteins, only the hephaestinlike protein shared a common origin for these coral species.
Meta pr oteomics has the ability to study the holobiont as a whole (Maron et al. 2007 ), while also permitting the identification of a protein's source without the need for host and microbial separ ation.The spectr a, once pr ocessed, can be matc hed a gainst databases , high quality transcriptomes , or genomes to link the expr essed pr otein to a particular species, and ther efor e anal yze host-micr obial inter actions.Micr obial pr oteins and their pr oducts affect host physiology and other microbes present in the microbiome (Schweppe et al. 2015, Rolig et al. 2018 ), so the application of meta pr oteomics to identify and quantify micr obial pr oteins permits a more accurate depiction of the metabolic functions of the holobiont.Large-scale studies to identify and quantify proteins expressed by the coral holobiont through high-resolution MS is a fairl y r ecent method emplo y ed b y cor al r esearc hers, thus far mainly to the coral host and algal symbionts (Petrou et al. 2021, Pei et al. 2022, Sun et al. 2022 ).Petrou et al. ( 2021 ) utilized tandem MS to determine that proteins related to photosynthesis and energy production were downregulated without signs of o xidati ve stress, despite lipids stores in the symbiont increasing 2-fold, indicating the symbiont responded to heat stress by reducing carbon translocation to the coral animal.In another study, tandem mass tag labeling and nano LC-MS/MS analysis was used to determine larval and symbiont response to thermal stress and p CO 2 (Sun et al. 2022 ).Again, man y photosynthesis-r elated pr oteins wer e downr egulated in the symbionts under thermal stress [photosystem (PS) I reaction center subunits IV and XI, oxygen-evolving enhancer], in addition to pr oteins pr esent in the Calvin cycle (phosphoribulokinase) and the C 4 pathway (phosphoenolpyruvate carboxylase), suggesting symbionts reduce carbon fixation when exposed to ele v ated temper atur es.In contr ast, under high p CO 2 conditions, the expression of PS I iron-sulfur center proteins were upregulated.These findings were corroborated by Krämer et al. ( 2022 ).
Ther e ar e instances of a ppl ying pr oteomics to individuall y cultured members of the microbiome (Chan et al. 2022, Pogoreutz et al. 2022 ).Thr ough pr oteomics, it w as sho wn that Endozoicomonas montiporae , a coral bacterial group that rapidly decreases in abundance during thermal stress events, positively expressed heat shoc k pr oteins and negativ el y expr essed man y antioxidant defense proteins (Chan et al. 2022 ).Endozoicomonas montiporae cultur es wer e also incubated with cor al l ysates and exposed to heat str ess.Some pr oteins wer e onl y differ entiall y expr essed with the combination of thermal stress and host presence, signifying E .montiporae protein expression is affected by heat-induced host factors.In a second species of Endozoicomonas , E .marisrubri , proteomic response to tissue extracts of its native host, Acropora humilis , was assessed.Pogoreutz et al. ( 2022 ) found that vitamin B1 and B6 biosynthesis, as well as gl ycol ytic pr ocesses, wer e upr egulated in response to holobiont metabolism, suggesting that symbiotic lifestyles of Endozoicomonas involve a modulation of host immunity.
Se v er al platforms and tools exist to process metaproteomic data for all biological systems beyond model or ganisms, suc h as Galaxy-P (Ja gta p et al. 2015 ), MetaPr oteomeAnal yzer (Muth et al. 2018 ), MetaQuantome (Easterly et al. 2019 ), and PromMetaLab (Cheng et al. 2017 ).Ne v ertheless, pr oteomics in gener al does not provide information about expressed noncoding elements or what genes are involved in the production or regulation of proteins (Gr av es and Haystead 2002 ), and only a subset of the total proteome can be measured at a time (Beck et al. 2011 ).Metaproteomic studies involve additional challenges.To distinguish between host and microbial proteins, gene sequences specific to the host and micr obiome ar e needed, yet host and undigested food proteins can contaminate the microbial proteome (Isaac et al. 2019 ).Additionall y, ther e is not a coral microbiome protein database, so many detected microbial proteins will not be identified with the use of general databases (Charubin et al. 2020 ).Even with highly populated microbial protein databases, an estimated 32%-40% of micr obial pr oteins ar e annotated as "unknown function" (Goodacr e et al. 2014, Poudel et al. 2021 ) expanding complications with regards to the analysis of microbial metaproteomic data.

Holobiont metabolomics and identifying metabolite origins
Metabolomics is a r a pidl y emer ging field pr oviding a dir ect, functional readout of an organism's physiological state (Geier et al. 2020 ) through a combination of strategies to identify and quantify cellular metabolites.Metabolomics employs sophisticated analytical instruments with statistical and m ultiv ariant methods to extract data for interpretation (Roessner and Bowne 2009 ).There exists a strong correlation between metabolites and the phenotype (Fiehn 2002 ), enabling meaningful studies of responses to biotic and abiotic stress.
Metabolomics has helped determine chemical signatures of bioc hemical r esponses in the holobiont (Raina et al. 2013, Hillyer et al. 2017, 2018, Lohr et al. 2018, 2019, Far a g et al. 2018, Ochsenku¨hn et al. 2018, Williams et al. 2021a ).Through polar metabolic profile analysis of Montipora capitata and P. acuta collected through high performance liq-uid c hr omatogr a phy-mass spectr ometry (HPLC-MS), a collection of dipeptides (ar ginine-glutamine, alanine-ar ginine, v alinear ginine, and l ysine-glutamine) pr oduced during thermal str ess was discov er ed (Williams et al. 2021a ).The data r ecov er ed was also able to expand on pr e viousl y known coral derived metabolites, montiporic acids (MAs), finding that MAs were present in coral tissue, not just coral eggs, and produced by species outside of the Montipora genus (Hagedorn et al. 2015 ).Another method, gas-liquid partition c hr omatogr a phy (GLPC), coupled with a 13 C isotope tracer was utilized to tr ac k autotr ophic carbon during exposure to thermal stress intended to result in bleaching (Hillyer et al. 2018 ).Fluctuations of carbohydrate and fatty acid metabolism, lipogenesis, and homeostatic responses to thermal, o xidati ve, and osmotic str ess wer e observ ed.Though it was widel y accepted that algal symbionts were responsible for dimethylsuphoniopropionate (DMSP) production in the holobiont (Stefels 2000 ), which subsequentl y serv es as a sulfur and carbon source for marine bacteria (Todd et al. 2007 ), DMSP was found in asymbiotic coral juv eniles thr ough the a pplication of quantitativ e nuclear ma gnetic r esonance (qNMR) anal ysis (Raina et al. 2013 ).DMSP le v els in ne wl y settled asymbiotic cor al juv eniles incr eased up to 54% o ver time , and once juv eniles wer e exposed to thermal stress, this le v el incr eased up to 76%.
Metabolomic studies have the po w er to illuminate the complex, m ultifarious biological pr ocesses, whic h define the coral holobiont's essential, r esponsiv e, and ada ptiv e functions, but an y subset of metabolomics is intrinsically challenging due to the complexity at the heart metabolomic studies.Sample pr epar ation, the reactivity of primary and secondary metabolites, structur al div ersity, peak misidentification, and "dark" metabolites dictate what metabolites are detected and usable for analysis (Lu et al. 2017 ).This is further complicated by the holobiont structure, whic h r ests upon metabolic exchange amongst holobiont members and is itself a response to symbiosis (Stat et al. 2008 ).Separating holobiont members for individual metabolomic studies still cannot r esolv e, whic h member of the holobiont is responsible for the production of a given metabolite.Ho w ever, this can be answered through applying approaches, which utilize labeling, such as stable isotope probing, pulse chase experiments, isotope arra ys , and bioorthogonal noncanonical amino acid tagging.These techniques may detect biochemical transformations of the micr obiome, suc h as identifying activ e micr obial participants and ascertaining the r elativ e contributions the host and microbiome have on carbon, nitrogen, or sulfur cycling (Engelberts et al. 2021 ), but they cannot determine the directionality of metabolite allocation.
Resolving substrate directionality may be possible through the advancements in microscopy techniques, such correlative light and electr on micr oscopy and light sheet fluor escence micr oscopy (LSFM), which combine 3D in situ microscopic imaging with chemical and antibody probes to profile and visualize cells under physiological conditions, such as cell proliferation, hypoxia, or apoptosis (Geier et al. 2020 ).For example, LSFM produces images of thin optical sections at high speed and is suitable for observing the inner arc hitectur e of the holobiont without physical sectioning to examine species composition, r elativ e abundances, and physiologies of microbiome members through in situ hybridization of DNA probes coupled with immunofluorescence of antibodies specific to k e y pr oteins or enzymes r esponsible for tar get metabolite pr oduction (P arthasar athy 2018 ).These a ppr oac hes would also boast the opportunity of ac hie ving a mec hanistic understanding of the fundamental processes by which the coral host forms and maintains symbiotic associations with inter-and intracellular micro-bial species, in addition to how these metabolic responses alter during stress and dysbiosis.As LSFM would be hindered by the opaqueness of the skeleton, this a ppr oac h may be best suited with the coral model Aiptasia , or with coral larvae.

Network-based omics data integration
Network-based methods have been commonly applied in the coral field and hold great promise for analyzing large datasets by shrinking the dimensionality of the data .Network biology uses tools deriv ed fr om gr a ph theory to construct useful data structures between pairs of components in a system to investigate the interactions or relationships between said components (Koutrouli et al. 2020 ).These associations are illustrated as edges connecting the components, with components r epr esented as nodes, and gr a ph measur es suc h as betweenness, degr ee, and centr ality used to inter pr et meaningful biological r elationships (Emmert-Str eib and Dehmer 2011 ).
From a methodological perspective, networks are defined by their incor por ation of Bayesian methods.Bayesian networks are composed of the gr a ph and a local probability model incorporating a priori data (e.g.data probability distribution, and parametric or nonparametric) into the modeling scheme (Heckerman 1998 ).NonBayesian (NB) networks are either constructed by correlation anal ysis (Menic hetti et al. 2014 ) or utilize inter actions defined by molecular data (Aerts et al. 2006 ).From a biological perspective, networks may utilize known interactions (PPI networks; Bersanelli et al. 2016 ), but this is not necessary for all network types (coregulation and coexpr ession).Networks, whic h do not r el y on known interactions specific to a given species are especially useful in corals because of the current lack of supporting data available to nonmodel species (Williams et al. 2023 ).These include gene and pr otein coexpr ession networks, metabolite corr elation networks, and microbial-specific networks .T he following sections will discuss how the different network types have been applied to coral r esearc h to understand the coral holobiont interactions, as well as c har acterize ne w metabolites and identify areas lacking within cor al r esearc h.

Gene and protein coexpression networks
Gene coexpression networks have become a common approach in the coral field (Wright et al. 2015, Reyes-Bermudez et al. 2016, Rose et al. 2016, Ruiz-Jones and Palumbi 2017, Thomas et al. 2019, Alv es Monteir o et al. 2020, Dixon et al. 2020, MacKnight et al. 2022 ) due to the emergence of the WGCN A (w eighted gene coexpression network analysis) R package (Langfelder and Horvath 2008 ).WGCNA constructs NB coexpr ession networks thr ough pairwise correlations and eigengenes, where nodes correspond to gene expr ession pr ofiles and edges r epr esent gene expr ession pairwise correlations.Module assignment is achieved through hierarchical clustering of the expression data.The eigengene of each module can be correlated to the sample traits (e .g. genotype , experimental condition, and location) to identify modules of interest.Eigengenes themselves can be considered a re presentati ve of their given module's gene expression profile, greatly reducing the size and complexity of transcriptomic data (Langfelder and Horvath 2008 ).
With Acropora digitifera , Reyes-Bermudez et al. ( 2016) performed quantitativ e RNA-seq anal ysis on embryonic, larv al, and adult samples.WGCNA anal ysis identified thr ee coexpr ession modules of interest; during embryonic and larval transitions, modules corresponded to cellular fate and morphogenesis, whereas the tran-sition of larval bodies to adult stages is defined by a switch in lifestyle and regulating polyp processes.MacKnight et al. ( 2022 ) found modules correlated to lesion pr ogr ession r ates of se v en Caribbean coral species infected with white plague disease were dominated by immune responses and cytoskeletal arrangement.When applied to gene expression profiles of Acropora hyacinthus colonies collected following a natur al bleac hing e v ent, some module eigengenes reflected expression patterns impacted 12 months after the e v ent (Thomas and Palumbi 2017 ).
By slightly augmenting WGCNA, coexpression networks can be built for proteomic data (Zhang et al. 2016, 2018, Pei et al. 2017, Nishim ur a et al. 2021 ).The same basic concepts of WGCNA appl y to pr oteomics; pr otein or peptide expr ession pr ofiles r epr esent nodes and edges correspond to pairwise correlations.A comprehensive understanding of WGCNA and the proteomic data itself is recommended to correctly implement the methods to construct a pr oteomic coexpr ession network.For instance, the "goodSam-plesGenes" function should be applied to r emov e pr oteins with > 50% of missing entries (Wu et al. 2020 ) and signed networks, wher e positiv el y corr elated pr oteins that corr espond to modules, rather than unsigned networks, are suggested (Langfelder and Horv ath 2008 ).Mor eov er, pr otein-based and peptide-based pr oteomic datasets have to be treated differently, as peptide-based datasets may result in concordant protein modules (Gibbs et al. 2013 ), but protein-based datasets ma y ha v e r elated pr oteins cluster separ atel y due to differ ent ov er all r egulation tr ends (Pei et al. 2017 ).

Metabolomic networks
Experimental network strategies for metabolomic data include association networks (NB) and molecular similarity networks (Bayesian;Amara et al. 2022 ).Metabolite correlation networks use quantitativ e information, suc h as normalized ion counts, to r epr esent inter actions (edges) between metabolites (nodes).In these networks, nodes are metabolic intermediates in biological pathwa ys , possibly steps in biological synthesis or demonstrating a dependence between m ultiple pr ocesses (Mor genthal et al. 2006 ).Gener all y, corr elation networks serve as a wonderful complement to measuring discrete metabolite le v els because they analyze the complete dataset and provide links between metabolic pathways (Perez De Souza et al. 2020 ).The major advantages of applying this method to the coral holobiont is that the pr e vious ma pping of biological pathways and metabolic r elationships ar e not r equir ed.As already discussed, identifying metabolic origins, as well as specific microbial holobiont members, at a given time is a difficult feat.This a ppr oac h could generate holobiont-specific metabolic interactions to be validated by future studies without much preceding information, although the addition of other omics data can help strengthen hypothesis generation (Williams et al. 2021a ).
Correlation networks applied to untargeted metabolomic data are most useful when a significant number of metabolites in the network ar e c hemicall y defined.Unfortunatel y for the coral holobiont, the majority of its compounds have a le v el four identification, or are unknown and do not match to databases through conv entional tec hniques (Salek et al. 2013 ).Molecular similarity networks, ther efor e hav e a place in cor al r esearc h when MS is used because they aid in the identification of unknown compounds by assuming similar fr a gmentation patterns r esult fr om r elated structur es (Ramos et al. 2019 ).For this reason, they can be thought of as spectra similarity networks, wher e spectr al pairs ar e compar ed by dot product calculations after vectorization in n -dimensions (Yang et al. 2013 ).The cosine angle connecting the vectors then dictates the degree of spectral similarity.A value of one r epr esents an identical match, but any value above 0.7 will set significant edges and produce modules that contain metabolites capable of attaining identification through their association with similar, known metabolites (Perez De Souza et al. 2020 ).
The Global Natural Products Social Molecular Networking database, first curated by Wang et al. ( 2016 ), provides a platform for open-source mass spectral data sharing that has been incor por ated into processing tools capable of producing molecular networks, such as mzMine2 (Pluskal et al. 2010 ), OpenMS (Röst et al. 2016 ), MeMSChem (Hartmann et al. 2017 ), and featurebased molecular networking (FBMN; Nothias et al. 2020 ).FBMN has been incor por ated into cor al studies to show an enrichment of tyr osine deriv ativ es , oleoyl-taurines , and acyl carnitines in the coral exometabolome compared to the algal exometabolome, demonstr ating a mec hanism for r eef nitr ogen and phosphor ousr ecycling (Wegley K ell y et al. 2022 ) and the accumulation of betaine lipids with varying degrees of saturation produced by symbionts in corals that have never bleached compared to apparently healthy corals that had bleached 4 years prior, possibly due to betaine lipids with a higher degrees of saturation providing increased thermal stability to algal membranes (Roach et al. 2021 ).

Pr otein-pr otein interaction networks
The STRING (Search Tool for the Retrie v al of Inter acting Genes; Heidelber g, German y) database (Szklarczyk et al. 2022 ) is commonly used to generate protein-protein interaction (PPI) networks (Bayesian).STRING does support the coral species Stylophora pistillata , but these interactions do not have supporting data at this time and, to date, not man y cor al anal yses hav e r elied on PPI networks produced via STRING (Maor-Landaw et al. 2014, Kaniewska et al. 2015, Ishibashi et al. 2021 ).Wong et al. ( 2021 ) generated a PPI network with proteomic data from Platygyra carnosa affected by coral skeletal growth anomaly, but annotated proteins wer e ma pped using the human PPI database, rather than a coralspecific database.

Coral research network analysis
One additional study worth mentioning constructed undirected w eighted netw orks to visualize 50 y ears of resear ch related to coral disease (Montilla et al. 2019 ).For this, the disease studied, host genus, marine ecoregion, and r esearc h objectiv es wer e set as nodes , or vertices , and their weighted co-occurrence (i.e.cooccurr ence fr equency) within a pa per visualized as edges thr ough the R pac ka ge igraph (Csar di and Nepusz 2006 ).The netw ork sho w ed that almost half of r esearc h efforts focused onl y on fiv e diseases affecting five species in five locations.Black Band Syndrome (BBS) was by far the most studied disease, but mainly in Florida and parts of the Caribbean.Ultimately, the data demonstrated that a limited range of approaches, such as immune responses to Aspergillosis and 16S sequencing for BBS, rather than mor e compr ehensiv e studies , ha v e thus far been consider ed to understand each coral disease.

Microbial-specific networks
In microbiome studies, nodes can be biological (i.e .taxa, genes , and metabolites), environmental (i.e.pH, temper atur e, and r adiation), or host (i.e.antioxidant ca pacity, r espir ation, and calcification) features (Jiang et al. 2019 ).When applied to microbiome multiomics data, network methods can establish how informa-tion flows amongst community members and the entire coral holobiont (Röttjers and Faust 2018 ).For instance, networks can be constructed and visualized to illuminate micr obe-micr obe or micr obe-host inter actions (Faust and Raes 2012 ), associate micr obial members with metabolite production or cycling (Bouslimani et al. 2015 ), and ultimately lend perspective as to how environmental changes affect the microbial population (Kint et al. 2010, Alivisatos et al. 2015, Blaser et al. 2016, Maier et al. 2017 ).
Though there are few incidents of constructing networks to visualize aspects of the coral microbiome, those studies have provided insight into the dynamics of members who have evaded culturing techniques (Soffer et al. 2015, Lima et al. 2020, Maruyama et al. 2021 ).Soffer et al. ( 2015 ) utilized a network of phagebacteria interactions (microbial 16SrRNA amplicons and phage metagenomes) found to be associated with Orbicella annularis colonies from the US Virgin Islands experiencing an outbreak of white plague during a bleaching event.Six phage strains exclusiv el y inter acted with two bacterial species enriched in the diseased corals ( Rhodobacterales and Campylobacterale ), suggesting suc h pha ges could pr ovide pha ge ther a py during white pla gue outbr eaks to contr ol additional opportunistic bacterial infections.
In another instance, shotgun metagenomic data collected from inner and outer reef colonies of Pseudodiploria strigosa in Bermuda was used to construct microbial networks of the surface mucopol ysacc haride layer (SML) taxa (Lima et al. 2020 ).The networks for the SML microbiome at each reef zone were distinct fr om eac h other, with the outer r eef network being mor e tightl y connected compared to inner reef networks, indicating that the outer reef microbiomes have an increased community network structure.Ho w ever, the SML netw orks for the inner reefs demonstrated a higher betweenness centrality, or contained several microbial taxa ( Deinococci , Methanomicrobia , and Alphaproteobacteria ) significantly enabling the connectivity of the community network.T hese networks , along with simulations of the annual temperature fluctuations at each reef zone, were used to build a model of the SML microbiome .T he model ultimately sho w ed that microbial network profiles locally and generally highly impacted the SML microbiome, although not as strongly as temperature.

Non-network multiomics data integration appr oac hes
Ev ery le v el of omics data is a proxy for holobiont functions, but their individual analysis is limited to correlations, rather than reflecting causativ e pr ocesses.When omics data fr om mor e than one tec hnique ar e pr oduced for the same sample set, integration can provide useful insights into the flow of biological information within the system (Tarazona et al. 2018 ), and thus can help unr av el divisions between genotype and phenotype (Subramanian et al. 2020 ).There are numerous multiomics data integration revie ws cov ering differ ent integr ation tec hniques (Beale et al. 2016, Bersanelli et al. 2016, Hasin et al. 2017, Huang et al. 2017, Pierre-Jean et al. 2020, Picard et al. 2021, Kang et al. 2022, Maghsoudi et al. 2022 ) for each data type (Berger et al. 2013, Kristensen et al. 2014, Pinu et al. 2019, Graw et al. 2021, Jendoubi 2021, Kim and Kim 2021 ) and system (Tagu et al. 2014, Wang et al. 2019, Jamil et al. 2020, Son et al. 2020, Daliri et al. 2021, Layton and Bradbury 2022 ) fr om whic h the cor al field can dr a w knowledge .Examples of host-microbial data integration from the coral field, along with three tools designed for nonmodel organism data integration are outlined below.

Host-microbial data integration
The combination of microbial identification and host metabolic activity can uncover how the host's phenotype is influenced by microbial species (Zhang et al. 2019 ).Microbial and animal integr al syner gies ar e not limited to corals, allowing coral research to prosper by looking to other systems for guidance.Human-microbiome studies have generated three integrative a ppr oac hes: meta genomics to metatr anscriptomics, meta pr oteomics, and metabolomics (Zhang et al. 2019 ).When studying indigenous microbial communities of the holobiont, integrating micr obial meta genomics with host omics data can advance biological inter pr etation of biological pathwa ys , organic fluxes , and symbiotic relationships associated with specific microbial partners (DeSalvo et al. 2010, Daliri et al. 2021, Guo et al. 2021, Santoro et al. 2021, Savary et al. 2021, Voolstra et al. 2021, Lin et al. 2022, Rädecker et al. 2022 ).
In a landmark study, DeSalvo et al. ( 2010 ) showed that transcriptomic states of the coral Montastraea faveolata collected under thermal str ess cluster ed mor e str ongl y based on symbiont genotype and physiological par ameters r ather than experimental condition.Mor e r ecentl y, Voolstr a et al. ( 2021) compar ed gene expr ession (coral and algae) and bacterial community composition of robust corals from the northern and central Red Sea (NRS and CRS, r espectiv el y).The cor al and algal tr anscriptomic r esponses of NRS colonies wer e str ong and the bacterial comm unity assembla ges c hanged consider abl y.This w as supported b y separate findings that gene expression and microbiome composition of S. pistillata collected from the NRS rapidly responded to thermal stress, but > 94% and > 71% of coral and algal genes, r espectiv el y, r eturned to their baseline le v els during r ecov ery, as long as thermal stress did not exceed 32 • C (Savary et al. 2021 ).At temper atur es abov e 34 • C, cor al mortality r eigned and micr obial div ersity dwindled, becoming dominated by opportunistic species, demonstrating a lethal threshold for the most stress resistant species in the NRS.

Tools for host-microbial data integration ad v ancement
Current bioinformatic w orkflo ws do not commonly support the integr ation of m ultiomics data le v els when anal yzing the micr obiome, especially for nonmodel organisms.gNOMO is an example of a new, complete bioinformatic pipeline designed for nonmodel or ganisms, ca pable of integr ating up to thr ee omics le vels (meta genomics, metatr anscriptomics, and meta pr oteomics) for the microbiome and host in parallel (Muñoz-Benavent et al. 2020 ).This type of tool could not only advance microbial population analysis, but allow for physiological investigations within the microbiome's biological context.gNOMO was applied to experimental data collected from the complex microbiome affiliated with the coc kr oac h Blattella germanica , whic h is compar able to the coral animal in that B .germanica has an endosymbiont in addition to an expansive gut microbiome, to determine the source (i.e.host or microbiome) of k e y enzymes present in the nitrogen metabolic pathway (Lopez-Sanchez et al. 2009, Carrasco et al. 2014 ).The results after integrating all three data levels proved that while the host does produce four k e y enzymes present in these pathwa ys , the microbiome possessed and expressed the majority of enzymes present in nitrogen metabolism (Muñoz-Benavent et al. 2020 ).
Bioc hemical r eactions ar e the connection between genes and metabolites .T his basic , fundamental fact of life can be exploited to advance knowledge of coral physiology and stress response.Metabolite Annotation and Gene Integration (MAGI) software integrates metabolomics and gene data sets into a biochemical reaction network.The software provides metabolite identification, gene annotation, and generates metabolite-gene associations using v arious r eaction databases (Erbilgin et al. 2019 ).MAGI was r ecentl y a pplied to data fr om a thermal str ess experiment (Williams et al. 2021b ).From the metabolites and genes differentiall y expr essed during thermal str ess compar ed to ambient conditions, a suite of r edox r eactions and antioxidant responses were found linked to thermal stress, possibly demonstrating how the coral animal attempts to limit o xidati ve stress .T he analysis also provided evidence of sex steroid dysregulation, which was especially useful to explain the disrupted spawning e v ent that occurred during the experiment.
IntegrOmics is a tool to integrate two types of omics data to visualize sample similarities and find correlations between datasets (Lê Cao et al. 2009 ).This tool relies on correlations between molecular components follo w ed b y sPLS (sparse partial least squares) r egr ession.One important r equir ement for this tool is that each omics dataset be gener ated fr om the same samples.Compared to other integration tools, integrOmics is implemented through R and r elativ el y simple with its methodology.Additionall y, ther e ar e no limits on the omics data types analyzed, making it a favorable tool for cor al m ultiomics integr ation.Man y examples of its usa ge exist in the liter atur e (Bassi et al. 2018, Li et al. 2022 ), especially for host-micr obiome studies (Ste wart et al. 2017, Sovr an et al. 2019, Xia et al. 2022 ).

Consider a tions for design of holobiont multiomics studies
Multiomics data analysis and integration are not possible without a compr ehensiv e experimental design from the onset.The design of a multiomics experiment includes identifying the questions a study is capable of answering, what types of data are needed, and an integr ation str ategy to anal yze, inter pr et, and visualize the data (Tarazona et al. 2018 , Santiago-Rodriguez andHollister 2021 ).Perspective and opinion papers discussing best practices and ideas to unify coral studies are available (McLachlan et al. 2020, 2021, Grottoli et al. 2021, Pratte and Kellogg 2021, Gómez-Campo et al. 2022, Nielsen et al. 2022, Thurber et al. 2022, van Woesik et al. 2022 ), and their suggestions related to multiomics studies are summarized below, with the addition of recommendations not present in coral literature.

Requirements for holobiont multiomic experiments
The first r equir ement for an y omics integr ation study is that all omics and meta data be collected from the same samples (Cavill et al. 2016 ).Lar ge enough samples should be c hosen to pr ocure sufficient amounts of each extract and whatever meta data is needed.Fr a gments , > 9 cm 2 , ha v e corr espondingl y been shown to impr ov e accur acy of bleac hing experiments (Nielsen et al. 2022 ).Although there is evidence that sampling mucus has a benign effect upon the coral (Zaneveld et al. 2016 ), it is not advisable to resample tissue from the same nubbin during time point analysis .T his is because clipping the nubbin will instigate an immune wound healing response that will obfuscate data analysis (Palmer and Traylor-Knowles 2012 ).Preserving the entire nubbin at once with limited contact is the soundest way to limit additional stress placed upon the nubbin and sample-sample variance.
Concerning tank experiments, as with nonmultiomics studies, an acclimatization period is crucial for experimental and contr ol cor als .T he initial clipping and handling will elicit a str ess r e-sponse.Further, cor als ar e ada pted to their giv en envir onment.This includes variables such as temperature, light, and pH.Even within a single colony, nubbins will be adapted to various light levels (Gómez-Campo et al. 2022 ).Nubbins need to be acclimatized to initial tank conditions (light, pH, and temper atur e), and a gain to the experimental conditions.Samples should be monitored for tank and experimental acclimatization.How monitoring is performed will vary depending on the study's limits and questions it is attempting to ask.Again, it is important to remember that clipping nubbins will cause a stress response, so either choosing techniques that would not utilize coral tissue, and thereby inflict stress on the animal, or collecting additional samples for measurements should be determined when planning the experiment.Regardless, it is vital to ensure samples are acclimated, using measur ements suc h as maxim um photoc hemical efficiency (Fv/Fm; Gómez-Campo et al. 2022 ).
Recent studies hav e demonstr ated the significant le v el of div er gence between genotypes of the same species (Stephens et al. 2021 ).For this reason, it is recommended that genotypes not be mixed within time points or conditions.Enough nubbins from a single genotype should be available for enough replicates to be collected from each timepoint under every given condition.Ho w e v er, r epeating the experiment with multiple genotypes will help to unr av el how genotype affects the data (Lohr et al. 2019, Williams et al. 2021a, 2023 ).
Omics data cannot be accur atel y anal yzed without pr oper metadata to define the biological states of each sample.Some covariate data such as symbiont genotype identification via qPCR (Silverstein et al. 2015 ), holobiont light absorption efficiency (Scheufen et al. 2017a, Krämer et al. 2022 ), c hlor ophyll a concentration (Jones 1997 ), and the rate of respiration, photosynthesis, and calcification (Carlot et al. 2022 ) can be collected from each sample in real-time.In the same vein, it is important to accur atel y identify the experimental phenotype, such as degree of bleaching, disease state, life stage, and anything else, i.e. r ele v ant to the study or can confound the results.For instance, recent studies have proposed and confirmed the utility of defining coral bleaching as the phenotype where coral photosynthesis is fully suppressed (Scheufen et al. 2017a,b , Krämer et al. 2022 ).
Ho w e v er, some crucial information can only be obtained through using colonies maintained and follo w ed for years .T his includes a colony's bleaching history, which has been shown to impact coral and algal metabolism for over 12 months following bleaching (Thomas and Palumbi 2017 ), its normal adapted conditions (i.e.temper atur e r ange, normal pollution le v els, and r adiation intensity; Grottoli et al. 2020 ), its seasonal changes (Scheufen et al. 2017a ,b ), and its r epr oductiv e history.For this reason, when possible, it is advised to use colonies with historical data a vailable .
Another essential r equir ement with cor al m ultiomics studies is that metabolism is quenched in an identical and a ppr opriate manner for e v ery sample .T he complete pr eserv ation time of coral nubbins v aries dr asticall y depending on the method applied (Andersson et al. 2019 ) and amount of organic versus nonorganic material present, so fragments should be of similar size .Moreo ver, knowledge concerning the time a chosen metabolic quenching technique takes to thoroughly halt metabolism for the entire sample is useful to consider its effects on the omics techniques emplo y ed (i.e.small metabolite turnover rates are faster than RNA, whic h ar e faster than pr oteins and DNA), in addition to which downstr eam anal yses ar e possible with eac h pr eserv ation tec hnique (McLachlan et al. 2021, Thurber et al. 2022 ).For example, sna p-fr eezing a small coral fragment in liquid nitrogen will halt metabolism within 10 min, while slow freezing at −20 • will take a day, but neither method offer the potential for microscopy or ima ge anal yses because the ice crystals disrupt tissue structur e (Hagedorn et al. 2013 ).
Concerning time series experiments, depending on the question asked, the suitable timing between collections is imper ativ e.If a study aims to link mRNA to protein expression levels, the timescale of the experiment must be minutes to hours, rather than days to weeks (Vogel andMarcotte 2012 , Buccitelli andSelbach 2020 ).Like wise, because cor als slough off their m ucus as quic kl y as e v ery 3 hours depending on the species (Bessell- Browne et al. 2017 ), m ucosal micr obial div ersification studies should also follow a shorter timescale design.In comparison, it has been shown that endosymbiotic algal div ersity r emains fairl y constant (Cunning et al. 2015 ), so longer time series experiments concerning the symbiont's response to environmental perturbations are less complicated.Regardless of the experimental design, time points should be chosen with circadian cycles (i.e.daily sample collections should be performed at the same time; Tarzona et al. 2018 ) and seasonal changes (Scheufen et al. 2017b ) in mind.
The necessary experimental conditions to not only illicit a measurable biological response, but a response to realistic stressor intensities, either at current or future levels, should be considered.This will vary depending on each body of water, as well as seasonal c hanges (Sc heufen et al. 2017b ) and the exact location of the coral colonies utilized in each experiment (K ell y et al. 2014(K ell y et al. , Bar ott et al. 2021 ) ).Environmental conditions assayed in coral studies are typically conducted under peak sea surface conditions predicted for the end of the 21st century, whereas models project complex envir onmental v ariability (Ziegler et al. 2021 ).Temper atur es will be w armer y ear-round, with longer w arming e v ents during summer months and hourly temperature and p CO 2 fluctuations extending beyond the pr ojected av er a ges cor als ar e expected to face within 100 years (IPCC et al. 2014 ).For tank experiments, including these diurnal fluctuations would mor e accur atel y mimic the holobiont's response and adaptation to environmental perturbations.

Conclusion
The coral holobiont possesses broad variation in stress tolerance tr aits that natur al selection can act upon (Wright et al. 2019 ).Presumabl y, the micr obiome and symbionts encompass a significant portion of such variation in stress tolerance, adaptation, and evolution.The complexity of the holobiont and the ecosystem it creates can benefit from a holistic view captured through multiomics data, but integr ating v arious omics data is not without complications .Experimental constraints , a lack of high-quality genomes, a significant number of dark genes and metabolites, difficulties sequencing and culturing microbial species, analytical challenges, and issues imposed by separating holobiont members are but a few.A major obstacle facing the coral field will be learning how to effectiv el y integr ate omics data fr om all members of the holobiont to understand the system, rather than only its individual parts.Ho w e v er, as our po w er to generate and anal yze m ultiomics data has grown, it has become clear that there is no one-size fits all a ppr oac h to understanding holobiont inter actions and its r esulting health.

Ac kno wledgments
The author would like to thank Dr Max Haggblom and the two anon ymous r e vie wers for their constructiv e comments and suggestions, whic h gr eatl y impr ov ed this r e vie w.